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Qatar Construction Surge Capacity Forecaster

Qatar's construction sector is poised for a 6.7% surge driven by strategic investment projects. Build a Python demand forecasting agent that processes tender announcements, raw material import statistics, and labor capacity indices to predict supply bottlenecks. Achieve accurate forecasts with under 4% mean absolute percentage error.

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What you are building

Develop an predictive analytics agent in Python to forecast supply chain bottlenecks across Qatar's expanding construction sector.

How work is evaluated

Evaluation

Evaluates demand forecast accuracy against benchmark material requirements.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

growth_scaling_check

Ensures forecast accounts for reported 6.7% sector surge

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

mape_score

Mean Absolute Percentage Error relative to ground truth demand • target: 3.8 • range: 0-100

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Aggregate sector-wide construction tender values and material volume metrics.

  • Apply time-series demand forecasting models (ARIMA / Prophet / XGBoost).

  • Identify material shortfalls (rebar, cement, aggregate) in advance.

  • Generate early warning dashboards for procurement officers.

Resources

Dataset notes

Historical macro-construction data and tender pipelines from Qatar infrastructure authorities.

Challenge setup

Evaluates demand forecast accuracy against benchmark material requirements.

Challenge input

JSON containing quarterly growth rate, tender pipeline value, and baseline demand

Python Data Stack

Provides numerical modeling and time-series logic.

Evaluated output

JSON with predicted rebar demand, shortfall risk level, and confidence interval

Evaluation checks
  • Ensures forecast accounts for reported 6.7% sector surge
  • Mean Absolute Percentage Error relative to ground truth demand • target: 3.8 • range: 0-100
Configured evaluation evidence
  • Benchmark: DemandForecastBench
  • Mape Score target: 3.8
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • deepchecks · Required
  • Fiddler AI · Optional
  • LiveKit · Optional

Evaluation contract

  • growth_scaling_check · Weight 1
  • mape_score · Weight 1

Metrics

  • mape_score · Target 3.8

Benchmarks

  • DemandForecastBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing quarterly growth rate, tender pipeline value, and baseline demand

    Output: JSON with predicted rebar demand, shortfall risk level, and confidence interval

    Execution harness: Python

Evaluation tools

  • Python Data Stack · Forecasting Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Qatar Construction Surge Capacity Forecaster